Describing Videos by Exploiting Temporal Structure

TL;DR

The paper tackles open-domain video description by addressing the dual temporal structure of videos: local motion within short frame sequences and global temporal ordering of events. It introduces a 3-D CNN to capture local spatio-temporal cues and a temporal attention mechanism to leverage global structure, integrating both within an encoder--decoder framework with an LSTM decoder. Empirical results on Youtube2Text and the larger DVS dataset show that combining local and global temporal modeling yields the strongest performance across BLEU, METEOR, CIDEr, and perplexity, with qualitative attention visualizations supporting the interpretability of the model. This work advances open-domain video captioning by effectively incorporating temporal structure and motion-aware representations, offering improvements in automatic description quality and practical applicability for video indexing and accessibility.

Abstract

Recent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are static, working with videos requires modeling their dynamic temporal structure and then properly integrating that information into a natural language description. In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions. First, our approach incorporates a spatial temporal 3-D convolutional neural network (3-D CNN) representation of the short temporal dynamics. The 3-D CNN representation is trained on video action recognition tasks, so as to produce a representation that is tuned to human motion and behavior. Second we propose a temporal attention mechanism that allows to go beyond local temporal modeling and learns to automatically select the most relevant temporal segments given the text-generating RNN. Our approach exceeds the current state-of-art for both BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on a new, larger and more challenging dataset of paired video and natural language descriptions.

Figures (19)

• Figure 1: High-level visualization of our approach to video description generation. We incorporate models of both the local temporal dynamic (i.e. within blocks of a few frames) of videos, as well as their global temporal structure. The local structure is modeled using the temporal feature maps of a 3-D CNN, while a temporal attention mechanism is used to combine information across the entire video. For each generated word, the model can focus on different temporal regions in the video. For simplicity, we highlight only the region having the maximum attention above.
• Figure 2: Illustration of the spatio-temporal convolutional neural network (3-D CNN). This network is trained for activity recognition. Then, only the convolutional layers are involved when generating video descriptions.
• Figure 3: Illustration of the proposed temporal attention mechanism in the LSTM decoder
• Figure 4: Four sample videos and their corresponding generated and ground-truth descriptions from Youtube2Text (Left Column) and DVS (Right Column). The bar plot under each frame corresponds to the attention weight $\alpha_i^t$ for the frame when the corresponding word (color-coded) was generated. From the top left panel, we can see that when the word "road" is about to be generated, the model focuses highly on the third frame where the road is clearly visible. Similarly, on the bottom left panel, we can see that the model attends to the second frame when it was about to generate the word "Someone". The bottom row includes alternate descriptions generated by the other model variations.
• Figure 5: Illustration of the spatio-temporal convolutional neural network (3-D CNN). This network is trained for activity recognition. Then, only the convolutional layers are involved when generating video descriptions.